Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
  • A61B5/02405—Determining heart rate variability
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
  • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
  • A61B5/36—Detecting PQ interval, PR interval or QT interval
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
  • A61B5/364—Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
  • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7271—Specific aspects of physiological measurement analysis
  • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient ; user input means
  • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
  • A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
  • A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient ; user input means
  • A61B5/7405—Details of notification to user or communication with user or patient ; user input means using sound
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient ; user input means
  • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

• the present invention relates to a method and apparatus for identifying hyperglycaemia, and in particular to a method and apparatus to sense physiological responses of a patient from parameters of an ECG tracing of a patient for early detection of hyperglycaemic conditions.
• Hyperglycaemia is a condition characterised by abnormally high blood glucose levels. It can lead to ketoacidocis which could be fatal.
• hypoglycaemia is the most common complication experienced by patients with Type 1 diabetes. If not treated properly, severe hypoglycaemia may result in coma and irreversible brain damage.
• BGL blood glucose levels
• Hypoglycaemia is often considered to be encountered when BGL ⁇ 3.33 mmol 1
• normoglycaemia is when 3.33 mmol/1 ⁇ BGL ⁇ 8.33 mmol/1
• hyperglycaemia is defined at different levels, for example BGL > 11.1 mmol/1.
• Non-invasive methods proposed up to date include systems such as: infrared/near-infrared spectroscopy, iontophoresis, skin conductance, etc. However, none of these have proved sufficiently reliable or unobtrusive.
• FIG. 1 a typical ECG (Electrocardiograph) tracing of a cardiac cycle (heartbeat).
• the ECG tracing typically consists of a P wave, a QRS complex, and, a T wave.
• the QT interval in particular reflects the duration of the intracellular action potential. It represents the time required for completion of both ventricular depolarisation and repolarisation. Recent studies indicate that insulin resistance affects the activation of the myocardium and can increase the QT interval. Because QT interval is influenced by chronotropic changes, Bazett defined the corrected QT interval (QTc), which is the measure generally used. QTc interval represents an index of myocardial refractoriness and electrical stability and it is associated with ventricular fibrillation and sudden cardiac death.
• TpTe interval from the peak of the T wave to its end
• TpTec corrected TpTe
• SDNN standard deviation of the RR interval index
• the present invention seeks to provide a non-invasive method and apparatus of identifying hyperglycaemic conditions in patients.
• the present invention also seeks to provide a method and apparatus for detecting hyperglycaemia which is relatively accurate, easy and inexpensive to use.
• the present invention also seeks to provide a hyperglycaemia detection apparatus which may, in a preferred form, trigger an alarm signal within an acceptable time delay from when this condition presents itself, such that appropriate remedial action may be taken in a timely manner.
• the present invention provides a method of determining hyperglycaemia, including the steps of:
• the method further includes any one or combination of the steps of:
• said output signal is provided to indicate hyperglycaemia based on a correlation between said rate of change of each of said QT interval and said TpTec interval.
• the method further includes the steps of:
• SDNN standard deviation of the RR interval
• the present invention provides a method of determining hyperglycaemia, including the steps of:
• the present invention provides an apparatus for determining hyperglycaemia including:
• output means to provide an output signal indicative of hyperglycaemia in the event that said rate of change of said QT interval reduces by a predetermined amount.
• the apparatus further determines the TpTec parameter of a patient
• said processor further determines any rate of change of said TpTec parameter
• said output signal is provided based on a correlation between said rate of change of said QT interval and said TpTec parameter.
• the apparatus further determines the SDNN of the RR interval of a patient
• said processor further determines any rate of change of said SDNN parameter
• said output signal is provided based on a correlation between said rate of change of any one or combination of said QT interval, said TpTec parameter, and/or said SDNN parameter.
• the present invention provides an apparatus for determining hyperglycaemia including:
• a sensor to sense at least one parameter, including heart rate, QT interval, TpTe, and/or SDNN;
• a processor to determine any rate of change of said parameter(s);
• output means to provide an output signal in the event of said rate of change of said parameter(s) being within a predetermined range.
• said processor includes a neural network to receive data obtained from said sensor(s), said neural network being programmed with an optimal learning algorithm.
• said neural network is programmed with an optimal Bayesian network.
• said output means includes an audio and/or visual alarm.
• Figure 1(a) shows an ECG of a normal sinus rhythms
• Figure 1(b) shows an ECG of a patient with a high BGL
• Figure 1 (c) shows an ECG of a patient with a normal BGL
• FIG. 1 shows typical changes in ECG parameters under hyperglycaemic conditions
• Figure 3 shows a flowchart describing the hyperglycaemia detection method/system of the present invention
• FIG 4 shows the blood glucose profiles of five type 1 diabetes (T1D) patients;
• Figure 5 shows the evidence framework for Bayesian inference;
• Figure 6 shows how the components may be typically attached to a patient.
• the inventor has identified that the onset of a hyperglycaemic condition results in changes to the ECG signal.
• Figure 1(b) shows the ECG of a patient having a high blood glucose level (BGL) of 9.81 mmol/1
• Figure 1(c) shows the ECG of the patient with a normal BGL of 4.87 mmol/1.
• ECG heart rate, QT interval, TpTe and SDN
• an optimal neural network provides a novel basis for early detection of hyperglycaemia, as well as an indirect measurement of blood glucose levels.
• factors which can affect the accuracy of the device such as environment conditions, stress, and the like.
• the device is capable of differentiating between effects caused by environmental conditions and those which initiate the presence of or onset of a particular medical condition.
• a suitable device may be used for the detection of conditions such as hyperglycaemia, or may be used to provide indirect measurement of blood glucose levels.
• Figure 2 shows typical changes in ECG parameters under hyperglycaemic conditions.
• an increase in PR is noted, a significant decrease in QTc, RTc, TpTec and SDNN are noted, but no significant changes in HR are noted.
• the present invention therefore provides a method and apparatus for effectively sensing these parameters, processing these sensed signals and providing an appropriate output, such that appropriate remedial action may be thereby taken.
• Figure 3 shows a flowchart describing the hyperglycaemia detection method/process of the present invention, which provides an output signal based on a correlation of the ECG parameters and their rates of change.
• the ECG may be achieved by placing three Ag-AgCl electrodes in a Leadll configuration on the patient's chest.
• the signal obtained from the electrodes may then be amplified using an instrumentation amplifier with gain of 10 and CMRR > lOOdB at 100Hz. This feeds through a high-pass filter with cutoff frequency of 0.5Hz.
• a second stage non-inverting amplifier may be added to provide a gain of 100.
• a bandpass filter may be used to detect the QRS complex of the ECG signal.
• a threshold circuit together with a comparator may be used to reliably detect the R slope.
• the QT interval is a clinical parameter which can be derived from the ECG signal.
• the normalised QTc interval Whilst it has been previously identified that during hypoglycaemia, the normalised QTc interval increases, the inventor has now also found that during hyperglycaemia, the normalised QTc interval decreases.
• QT measurement requires the identification of the start of QRS complex and the end of the T wave. The intersection of the isoelectric line and a tangent to the T wave can be used to measure the QT interval.
• the monitoring for hyperglycaemia and blood glucose level non-invasively is difficult due to imperfections caused by possible conflicting or reinforcing responses from various ECG parameters. This conflicting information is preferably handled in the framework of an optimal Bayesian network in order to obtain accurate determinations from a complex uncertain non-linear physiological system.
• a computational intelligence method of analysis is suitable for hyperglycaemia detection using a combination of one or more certain variables (heart rate, QT interval, TpTe, and standard deviation of the RR interval index (SDNN)).
• a Bayesian network is suitable for controlling complex systems.
• This neuro-estimator may be embedded in an EEPROM of the system to monitor hyperglycaemia episodes in patients.
• This neural network has a multilayer feedforward neural network structure with one input layer, one hidden layer and one output layer. Essentially, this neural network is trained using a learning algorithm in which synaptic strengths are systematically modified so that the response of the network will increasingly approximate the blood glucose status given by the available qualitative data.
• the inventor has tested responses from five T1D patients, and identified significant changes during the hyperglycaemia phase against the non-hyperglycaemia phase.
• the actual blood glucose profiles are shown in Figure 4.
• the overall data set consisted of a training set and a test set. For these, the whole data set which included both hyperglycaemia data part and non-hyperglycaemia data part were used.
• the framework for Bayesian inference was applied to the training set and it was found that the feedforward neural network architecture with 6 hidden nodes yielded the highest evidence, as shown in Figure 5.
• Communication between the sensors and the processor may be via a telemetric system, with radio frequency transmitter and receivers at typically 2.4 GHz). Other appropriate communication systems will be apparent to persons skilled in the art.
• the output may be provided in any appropriate format, such as an alarm or other visual or audible output.
• the alarm may be of any convenient type, and may include a simple radio alarm, a signal transmitted to a monitoring station, or the like.
• the data transmitted from the sensors may either be continuously logged, or monitored at appropriate discrete (short) intervals.
• the system may be typically interfaced with a PC which will continuously log the relevant data using a data management system such as Labview.
• a data management system such as Labview.
• the neural network algorithm needs not be of the type described herein, but any optimal neural network algorithm that is able to provide substantially real time analysis of multiple data streams in the manner described herein could be used.
• the present invention provides a noninvasive method of determining the presence or onset of the hyperglycaemic condition in a person.
• This method includes, continuously monitoring at least one or more ECG trace parameters of the patient; including, but not limited to heart rate, QTc interval, TpTec interval, and, standard deviation of the RR interval index (SDNN). It then establishes whether one or more of those monitored parameters changes, and if so, the rate of change of that parameter or parameters.
• Data obtained in the first two steps is preferably fed into a neural network programmed with an optimal algorithm.
• An output signal such as an alarm signal may be triggered when said neural network establishes conditions which suggest the presence or imminent onset of said hyperglycaemic condition.
• the monitoring of the heart rate, QT interval, TpTe and/or SDNN is preferably done with an ECG.
• the optimal learning algorithm may be based on a Bayesian neural network.
• the invention extends to apparatus for generating an alarm or other output when a hyperglycaemic condition is present or imminent in a person.
• the apparatus includes sensors for sensing at one or more of the heart rate, QT interval, TpTe, and SDNN. One or more of the parameters is monitored for change, and, the rate of its change.
• a neural network linked to said sensors may, for example, receive a substantially continuous data stream from said sensors.
• the neural network is programmed with an optimal learning algorithm and adapted to establish when the sensed parameters, and any change to those parameters, for a particular person are such as to indicate the presence or imminent onset of the physiological condition.
• An alarm of other output linked to said neural network is adapted to be triggered when the presence or imminent onset of said hyperglycaemic condition is established.
• the apparatus may include an optimal Bayesian network.
• the present invention therefore provides a method and apparatus for detecting a reduction in QT interval, monitoring its rate of change (dQT/dt) in a patient, and, if the rate of change reduces by a predetermined amount, provides an output signal which indicates a hyperglycaemic condition in the patient.
• the present invention also monitors for change in TpTe parameters, monitors any rate of change (dTpTe/dt) and, likewise, is processed to provide a corresponding output signal.
• the present invention also monitors for change in the standard deviation of the RR interval (SDNN), and, likewise is also processed to provide a corresponding output signal.
• SDNN standard deviation of the RR interval

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• 2013
  • 2013-08-29 CN CN201380057514.4A patent/CN104755025A/zh active Pending
  • 2013-08-29 WO PCT/AU2013/000970 patent/WO2014032105A1/en active Application Filing
  • 2013-08-29 EP EP13832488.4A patent/EP2890293A4/de not_active Withdrawn
  • 2013-08-29 AU AU2013308400A patent/AU2013308400A1/en not_active Abandoned
  • 2013-08-29 CA CA2882919A patent/CA2882919A1/en not_active Abandoned
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